.install_pkg | Installs Julia packages if needed |

.julia_project_status | Obtain the status of the current Julia project |

.set_seed | Set a seed both in Julia and R |

.using | Loads Julia packages |

BayesFluxR_setup | Set up of the Julia environment needed for BayesFlux |

bayes_by_backprop | Use Bayes By Backprop to find Variational Approximation to BNN. |

BNN | Create a Bayesian Neural Network |

BNN.totparams | Obtain the total parameters of the BNN |

Chain | Chain various layers together to form a network |

Dense | Create a Dense layer with 'in_size' inputs and 'out_size' outputs using 'act' activation function |

find_mode | Find the MAP of a BNN using SGD |

Gamma | Create a Gamma Prior |

get_random_symbol | Creates a random string that is used as variable in julia |

initialise.allsame | Initialises all parameters of the network, all hyper parameters of the prior and all additional parameters of the likelihood by drawing random values from 'dist'. |

InverseGamma | Create an Inverse-Gamma Prior |

likelihood.feedforward_normal | Use a Normal likelihood for a Feedforward network |

likelihood.feedforward_tdist | Use a t-Distribution likelihood for a Feedforward network |

likelihood.seqtoone_normal | Use a Normal likelihood for a seq-to-one recurrent network |

likelihood.seqtoone_tdist | Use a T-likelihood for a seq-to-one recurrent network. |

LSTM | Create an LSTM layer with 'in_size' input size, and 'out_size' hidden state size |

madapter.DiagCov | Use the diagonal of sample covariance matrix as inverse mass matrix. |

madapter.FixedMassMatrix | Use a fixed mass matrix |

madapter.FullCov | Use the full covariance matrix as inverse mass matrix |

madapter.RMSProp | Use RMSProp to adapt the inverse mass matrix. |

mcmc | Sample from a BNN using MCMC |

Normal | Create a Normal Prior |

opt.ADAM | ADAM optimiser |

opt.Descent | Standard gradient descent |

opt.RMSProp | RMSProp optimiser |

posterior_predictive | Draw from the posterior predictive distribution |

prior.gaussian | Use an isotropic Gaussian prior |

prior.mixturescale | Scale Mixture of Gaussian Prior |

prior_predictive | Sample from the prior predictive of a Bayesian Neural Network |

RNN | Create a RNN layer with 'in_size' input, 'out_size' hidden state and 'act' activation function |

sadapter.Const | Use a constant stepsize in mcmc |

sadapter.DualAverage | Use Dual Averaging like in STAN to tune stepsize |

sampler.AdaptiveMH | Adaptive Metropolis Hastings as introduced in |

sampler.GGMC | Gradient Guided Monte Carlo |

sampler.HMC | Standard Hamiltonian Monte Carlo (Hybrid Monte Carlo). |

sampler.SGLD | Stochastic Gradient Langevin Dynamics as proposed in Welling, M., & Teh, Y. W. (n.d.). Bayesian Learning via Stochastic Gradient Langevin Dynamics. 8. |

sampler.SGNHTS | Stochastic Gradient Nose-Hoover Thermostat as proposed in |

tensor_embed_mat | Embed a matrix of timeseries into a tensor |

Truncated | Truncates a Distribution |

vi.get_samples | Draw samples form a variational family. |